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Abstract Hydrogel-based tissue expansion combined with mass spectrometry (MS) offers an emerging spatial proteomics approach. Here, we present a filter-aided expansion proteomics (FAXP) strategy for spatial proteomics analysis of archived formalin-fixed paraffin-embedded (FFPE) specimens. Compared to our previous ProteomEx method, FAXP employed a customized tip device to enhance both the stability and throughput of sample preparation, thus guaranteeing the reproducibility and robustness of the workflow. FAXP achieved a 14.5-fold increase in volumetric resolution. It generated over 8 times higher peptide yield and a 255% rise in protein identifications while reducing sample preparation time by 50%. We also demonstrated the applicability of FAXP using human colorectal FFPE tissue samples. Furthermore, for the first time, we achieved bona fide single-subcellular proteomics under image guidance by integrating FAXP with laser capture microdissection.

Abstract Background The National Chest Pain Center Program (NCPCP) is a nationwide, quality enhancement program aimed at raising the standard of care for patients experiencing acute chest pain in China. The benefits of chest pain center (CPC) accreditation on acute coronary syndrome have been demonstrated. However, there is no evidence to indicate whether CPC accreditation improves outcomes for patients with acute aortic dissection (AAD). Methods We conducted a retrospective observational study of patients with AAD from 1671 hospitals in China, using data from the NCPCP spanning the period from January 1, 2016 to December 31, 2022. The patients were divided into 2 groups: pre-accreditation and post-accreditation admissions. The outcomes examined included in-hospital mortality, misdiagnosis, and Stanford type A AAD surgery. Multivariate logistic regression was employed to explore the relationship between CPC accreditation and in-hospital outcomes. Furthermore, we stratified the hospitals based on their geographical location (Eastern/Central/Western regions) or administrative status (provincial/non-provincial capital areas) to assess the impact of CPC accreditation on AAD patients across various regions. Results The analysis encompassed a total of 40,848 patients diagnosed with AAD. The post-accreditation group exhibited significantly lower rates of in-hospital mortality and misdiagnosis (12.1% vs. 16.3%, P

Automated Aerial Triangulation (AAT), aiming to restore image poses and reconstruct sparse points simultaneously, plays a pivotal role in earth observation. AAT has evolved into a fundamental process widely applied in large-scale Unmanned Aerial Vehicle (UAV) based mapping. However classic AAT methods still face challenges like low efficiency and limited robustness. This paper introduces DeepAAT, a deep learning network designed specifically for AAT of UAV imagery. DeepAAT considers both spatial and spectral characteristics of imagery, enhancing its capability to resolve erroneous matching pairs and accurately predict image poses. DeepAAT marks a significant leap in AAT’s efficiency, ensuring thorough scene coverage and precision. Its processing speed outpaces incremental AAT methods by hundreds of times and global AAT methods by tens of times while maintaining a comparable level of reconstruction accuracy. Additionally, DeepAAT’s scene clustering and merging strategy facilitate rapid localization and pose determination for large-scale UAV images, even under constrained computing resources. The experimental results demonstrate that DeepAAT substantially improves over conventional AAT methods, highlighting its potential for increased efficiency and accuracy in UAV-based 3D reconstruction tasks. To benefit the photogrammetry society, the code of DeepAAT will be released at: https://github.com/WHU-USI3DV/DeepAAT.

Tetrandrine (TET), a natural bisbenzyl isoquinoline alkaloid extracted from Stephania tetrandra S. Moore, has diverse pharmacological effects. However, its effects on melanoma remain unclear. Cellular proliferation assays, multi-omics analyses, and xenograft models were used to determine the effect of TET on melanoma. The direct target of TET was identified using biotin-TET pull-down liquid chromatograph-mass spectrometry (LC-MS), cellular thermal shift assays, and isothermal titration calorimetry (ITC) analysis. Our findings revealed that TET treatment induced robust cellular autophagy depending on activating transcription factor 6 (ATF6)-mediated endoplasmic reticulum (ER) stress. Simultaneously, it hindered autophagic flux by inducing cytoskeletal protein depolymerization in melanoma cells. TET treatment resulted in excessive accumulation of reactive oxygen species (ROS) and simultaneously triggered mitophagy. Sirtuin 5 (SIRT5) was ultimately found to be a direct target of TET. Mechanistically, TET led to the degradation of SIRT5 via the ubiquitin (Ub)-26S proteasome system. SIRT5 knockdown induced ROS accumulation, whereas SIRT5 overexpression attenuated the TET-induced ROS accumulation and autophagy. Importantly, TET exhibited anti-cancer effects in xenograft models depending on SIRT5 expression. This study highlights the potential of TET as an antimelanoma agent that targets SIRT5. These findings provide a promising avenue for the use of TET in melanoma treatment and underscore its potential as a therapeutic candidate.

To examine flavor variations in Xuanwei ham due to different cooking methods, we selected one-year cured Xuanwei ham and applied four techniques: dry frying (DF), baking (BA), steaming (ST), and boiling (BO). Organoleptic evaluation revealed ST received the highest overall sensory score. High-performance liquid chromatography (HPLC) revealed that the total nucleotide content was significantly different (P 1). This study aimed to support comprehensive research on the flavor characteristics of cooked Xuanwei ham and guide the selection of appropriate processing methods.

Accurate lane maps with semantics are crucial for various applications, such as high-definition maps (HD Maps), intelligent transportation systems (ITS), and digital twins. Manual annotation of lanes is labor-intensive and costly, prompting researchers to explore automatic lane extraction methods. This paper presents an end-to-end large-scale lane mapping method that considers both lane geometry and semantics. This study represents lane markings as polylines with uniformly sampled points and associated semantics, allowing for adaptation to varying lane shapes. Additionally, we propose an end-to-end network to extract lane polylines from mobile laser scanning (MLS) data, enabling the inference of vectorized lane instances without complex post-processing. The network consists of three components: a feature encoder, a column proposal generator, and a lane information decoder. The feature encoder encodes textual and structural information of lane markings to enhance the method’s robustness to data imperfections, such as varying lane intensity, uneven point density, and occlusion-induced incomplete data. The column proposal generator generates regions of interest for the subsequent decoder. Leveraging the embedded multi-scale features from the feature encoder, the lane decoder effectively predicts lane polylines and their associated semantics without requiring step-by-step conditional inference. Comprehensive experiments conducted on three lane datasets have demonstrated the performance of the proposed method, even in the presence of incomplete data and complex lane topology. Furthermore, the datasets used in this work, including source ground points, generated bird’s eye view (BEV) images, and annotations, will be publicly available with the publication of the paper. The code and dataset will be accessible through here.

Abstract Optical encryption strategies utilizing fully coherent light have been widely explored but often face challenges such as speckle noise and beam instabilities. In this work, we introduce a novel protocol for multi-channel optical information encoding and encryption using vectorial spatial coherence engineering of a partially coherent light beam. By characterizing the beam’s spatial coherence structure with a $$2 \times 2$$ 2 × 2 coherence matrix, we demonstrate independent control over the three components of the coherence Stokes vector. This allows for three-channel optical information encoding and encryption, with applications in color image representation. Unlike existing methods based on fully coherent light modulations, our approach utilizes a two-point dependent coherence Stokes vector, proving resilient to random noise in experimental scenarios. Our findings provide a robust foundation for higher-dimensional optical encoding and encryption, addressing limitations associated with partially coherent light in complex environments.

Quick and high-precision semantic segmentation from Mobile Laser Scanning (MLS) point clouds faces huge challenges such as large amounts of data, occlusion in complex scenes, and the high annotation cost associated with 3D point clouds. To tackle these challenges, this paper proposes a novel efficient and high-precision semantic segmentation method Mapping Considering Semantic Segmentation (MCSS) for MLS point clouds by leveraging the 2D-3D mapping relationship, which is not only without the need for labeling 3D samples but also complements missing information using multimodal data. According to the results of semantic segmentation on panoramic images by a neural network, a multi-frame mapping strategy and a local spatial similarity optimization method are proposed to project the panoramic image semantic predictions onto point clouds, thereby establishing coarse semantic information in the 3D domain. Then, a hierarchical geometric constraint model (HGCM) is designed to refine high-precision point cloud semantic segmentation. Comprehensive experimental evaluations demonstrate the effect and efficiency of our method in segmenting challenging large-scale MLS two datasets, achieving improvement by 16.8 % and 16.3 % compared with SPT. Furthermore, the proposed method takes an average of 8 s to process 1 million points and does not require annotation and training, surpassing previous methods in terms of efficiency.

Anti-human thymocyte globulin-Fresenius (ATG-F) is frequently utilized to achieve successful induction for kidney transplantation recipients. This study aimed to examine the association between the ATG-F dose-to-recipient-weight ratio (ADR) and the risk of developing urinary tract infections (UTIs) following kidney transplantation. Data of kidney transplant recipients who underwent ATG-F-induction peri-operatively in a medical center were retrospectively collected, and the incidence of UTIs during the first postoperative year was also recorded. The risk of UTI associated with ADR was analyzed, and receiver operating characteristic curves were drawn to determine the optimal ADR, followed by Cox regression models. In total, 131 recipients were included, with an UTI incidence of 19.08% and a mean interval of 3.08 months. The optimal ADR was 6.34, involving 41 and 90 patients in the low ADR and high ADR groups, respectively. The UTI-free rate in the low ADR group was significantly higher than that in the high ADR group (p = 0.007). Cox regression analysis indicated that a high ADR independently increased the risk of UTI following kidney transplantation (hazard ratio: 5.306, 95% confidence interval: 1.243–22.660, p = 0.024). There was no significant difference in rejection rate between the high ADR and low ADR groups. In conclusion, a high ADR increased the incidence of early postoperative UTI among kidney transplant recipients.

The utilization of synthetic aperture radar tomography (TomoSAR) technology for the three-dimensional imaging of urban areas has garnered significant attention. In the case of multi-pass spaceborne TomoSAR, eliminating the atmospheric phase screen (APS) is a critical step. At present, a two-tier network method is widely employed for APS removal from extensive regions. However, challenges persist in the construction of first-tier reference networks using the Delaunay triangulation and bridge reference network methods, such as low network coverage ratios and linear correlation problems associated with near-parallel bridges. Consequently, a notable portion of the inversion results for buildings or artificial structures are either absent or characterized by low accuracy. To fully harness the observational information from all small connecting networks, this study introduces an improved bridge reference network construction method based on a region-growing strategy. This method merges small subnetworks with surrounding reference networks using high-quality bridge connections to maximize the coverage of the largest reference network. To validate the feasibility and effectiveness of the proposed approach, 27 TerraSAR-X images, which were collected in Shenzhen, China were utilized to construct a reference network and estimate building heights. The results clearly indicate that the proposed method effectively overcomes the limitations of existing methods.

Mobile laser scanning (MLS) point clouds have increasingly been a significant data source for acquiring accurate three-dimensional (3D) semantic information from complex scenes. However, most current point cloud semantic segmentation methods heavily depend on a huge number of manually labeled training samples, which is labor-intensive and time-consuming. To address the above challenge, a weakly-supervised MLS point cloud semantic segmentation method integrated with data augmentation and active learning strategies is proposed (termed DAAL-WS). By taking advantage of our previously presented multi-branch weakly-supervised network (WSPointNet) in weakly supervisory signals and ensemble predictions, the DAAL-WS integrates WSPointNet with two essential components, i.e., an elevation-calibrated Mix3D (EC-Mix3D) data augmentation strategy and a point-level ensemble prediction-based (PLEP) active learning strategy. Specifically, the EC-Mix3D data augmentation strategy leverages elevation information to calibrate sub-point clouds and generates new contextual scenes by mixing the elevation-calibrated sub-point clouds, thereby augmenting the training point cloud distribution. Designed to reduce labeling redundancy, the PLEP active learning strategy selects the most important labeled points for model training. This strategy first measures the uncertainty for each unlabeled point by ensemble predictions and then employs a feature-distance suppression module to select the significant and discriminating unlabeled points for manual labeling. The proposed DAAL-WS method was evaluated on three public MLS datasets, including Toronto3D, Paris-Lille-3D, and WHU-MLS datasets, on which DAAL-WS obtained a competitive performance over fully-supervised baselines using only 0.015 %, 0.03 %, and 0.03 % labeled points, with mean Intersection over Union (mIoU) scores of 81.91 %, 82.59 %, and 60.36 %, respectively.

Underground coal gasification is currently the highest-temperature (over 1200 ℃) unconventional development method for fossil energy. Underground gasification of coal in the medium-deep (refer to the depth of 800～1500 m in this paper) has obvious advantages in improving the gasification pressure and reducing the geological safety risks. Scientific prediction of safe width of gasification cavity is important to ensure stable gasification operation. Since the method of calculating the safe width of gasification cavity based on Controlled Retreat Injection Point (CRIP) process has not yet been established, targeted research is needed to ensure the smooth implementation of field tests. Once the top plate of the gasification cavity is “exposed” to high temperature behind the gasification cavity. The generation position of thermal stresses inside the rock and the influence law of differences in the thermal expansion coefficient of particles and matrix on the magnitude of thermal stresses were studied by numerical simulation under compressive stress constraints. the thermal damage mechanism of rock under high temperature was clarified combined with the scanning electron microscope results of rock after high temperature treatment. According to the characteristics of CRIP gasification process, a thin plate model of gasification cavity roof considering the influence of high temperature was established, and a method for calculating the safe width of the gasification cavity was proposed in combination with the theory of “key layer”. The study revealed that, rock thermal damage was caused by the interaction and synergy of rock physicochemical reactions and thermal stress, and the rock undergone irregular deformation at high temperatures. Microstructural changes of rock caused by the thermal damage were the fundamental cause of changes in rock mechanical and physical properties. The maximum tensile thermal stress in rock occurred at the grain interfaces or in grains with small coefficient of thermal expansion. The maximum tensile thermal stress increased rapidly with decreasing the thermal expansion coefficient of the grain when the ratio of the thermal expansion coefficient of the grain to that of the matrix is in the range of [0.01−1). The microcracks of mudstone was developed when heated to 200 ℃, the crack development was more obvious when heated to 400 ℃, which mainly along the edge of the particles rupture. The number of cracks was increased and the size was larger when heated to 600−800 ℃. The larger cracks and numerous pores were formed when heated to 1000 ℃. The crack connectivity increased significantly at 1200 °C, and the stomatal development was larger. Due to the effect of high temperature, the step constant for the thin plate model is no longer a constant, and specific values need to be determined based on the extent of thermal damage to the roof of gasification cavity and the location of the hard rock layer on the top plate. The safe width of gasification cavity was affected by temperature. In the study, the calculated safe widths of sandstone roof at 35 and 1000 °C were 34.3 m and 14.1 m, respectively, with a difference of 58.9%. while the safe widths at 35 and 1000 °C were 16.7 m and 15.9 m, respectively, with a 4.8% difference. Lastly, a method of determining the longitudinal target area of coal seam was proposed from the perspective of reducing the risk of roof collapse and improving gasification control. When the coal seam thickness exceeded half of the gasification cavity safe width, it was suggested to design the longitudinal target area of horizontal well at a location not more than half of the safe width of gasification cavity from the top of the coal.

The geosynchronous-low-Earth-orbit bistatic synthetic aperture radar (GEO-LEO BiSAR) adopts the transceiver split system form, in which GEO SAR serves as the illuminator and LEO SAR serves as the receiver. GEO-LEO BiSAR can obtain abundant target scattering information, can realize large width imaging and reduce the receiver cost. Three parameters are selected as measurement indicators to comprehensively evaluate system performance and provide system design guidance. The spatial resolution can measure the imaging performance, the radiation resolution is the ability to distinguish the objects scattering characteristics, and the beam coverage area represents the system observation ability. They are all key parameters and are closely related to the BiSAR configuration. In this article, we use the generalized ambiguity function and integral equation model to derive the BiSAR spatial and radiation resolution expressions, and the beam coverage area calculation method is derived through geometric knowledge. These three parameters are modeled as objective functions for the system design of the multiobjective optimization problem, in which multiobjective evolutionary algorithm based on decomposition and differential evolution is used to solve the receiver orbital element. We can optimize these three parameters jointly considering their values or optimize the resolution (spatial and radiation resolution) and coverage area in sequence. Finally, the analysis correctness is verified by simulation experiments. The proposed system design method comprehensively considers the ability of observation, resolution and recognition, and can reasonably select joint or sequential optimization schemes according to the system capability requirements to guide the receiver orbital element selection.

As the key structure of industrial reducers, the planetary gear system will be subject to cyclic load and impact load during the operation, which will cause metal fatigue peeling off the surface of gears and form pitting fault. When the system is subject to the impact load, the severe fluctuation of vibration signal will disturb the fault identification of the system. To solve this problem, a diagnostic index is developed based on the dynamic characteristics analysis of the planetary gear system with pitting fault under impact conditions. Firstly, the contact stiffness model of gear pairs is established based on the analytic geometry method and the Hertz contact theory. Secondly, the impact load is introduced, and the contact force between sun gears and planet gears is solved by the impact function method. Finally, based on the spectrum distribution of the system dynamic response signal, the energy index of the sideband centroid is proposed, which realizes the accurate identification of the fault impact and the load impact.

Given its capacity to generate 2-D fine images of observation areas, very high resolution (VHR) synthetic aperture radar (SAR) has become increasingly popular in myriad fields, including remote sensing, geoscience, and surveillance. In this article, we present a comprehensive overview of VHR SAR systems and imaging to highlight the related research and promote further development. First, we provide a summary of VHR SAR applications and summarize the typical airborne and spaceborne systems. Subsequently, a detailed overview is provided to demonstrate the range VHR technologies by introducing dechirp processing, stepped frequency, and microwave photonic. In addition, VHR imaging methods on different platforms, i.e., ideal trajectory, airborne, and spaceborne, are reviewed. Finally, the challenges and prospects of VHR SAR are presented to inform future research directions. This work provides a valuable resource to improve the system design and signal processing of VHR SAR.

Semantic segmentation is an essential task in polarimetric synthetic aperture radar (PolSAR) image interpretation. To address the issue of insufficient measurement ability of single-view similarity, an unsupervised semantic segmentation method for PolSAR images based on multiview similarity is proposed to estimate the number-of-classes (NoC) and perform classification. NoC estimation is commonly neglected in semantic segmentation methods, to ensure rationality, a NoC estimation method before clustering is proposed based on multiview polarimetric rotation domain features and the visual assessment of tendency method for PolSAR images without supervision. Then, the norm distance, geodesic distance, maximum likelihood distance, and generalized likelihood ratio test distance based on the statistical characteristics of PolSAR images are comprehensively analyzed. Various advantages of different distances are integrated to combine the multiview vector information and scattering information to construct six multikernel similarity matrices. Subsequently, the consensus similarity network fusion method is utilized to further strengthen the discriminative ability of the similarity matrices. In addition, efficient superpixel segmentation is also adopted to reduce the speckle noise. Finally, based on the estimated NoC and the fused similarity matrix, spectral clustering is utilized to obtain semantic segmentation results. Extensive experiments are conducted on two AIRSAR datasets and one Gaofen-3 dataset demonstrate that the proposed method can effectively combine the spatial neighborhood similarity information and achieve higher semantic segmentation accuracy.

An L-band geosynchronous synthetic aperture radar (GEO SAR) is susceptible to the ionospheric scintillation induced by ionospheric irregularities due to its lower carrier frequency. In particular, the drifting characteristic of the ionospheric irregularities makes this issue more complicated because the drifting velocity is comparable with the scanning velocity of ionospheric penetration points (IPPs) of GEO SAR. In this article, a drifting ionospheric scintillation simulator (DISS) is devised for producing L-band GEO SAR raw data affected by the drifting ionospheric scintillation phase and amplitude errors. The DISS is divided into a static scintillation transfer function (STF) generator, an IPP drifting offset indexer and a GEO SAR echo generator. Each azimuth echo of a point target is modulated by the complex STF versus the drifting IPP coordinate. Finally, two simulation experiments of the point-target and extended-target scenes are conducted to validate the effectiveness of the DISS. The results indicate that the drifting ionospheric irregularities present absolutely different artifacts and more serious decorrelations in L-band GEO SAR images, compared with the traditional L-band low-Earth-orbit (LEO) SAR.

Wood-leaf separation from terrestrial laser scanning (TLS) is a crucial prerequisite for quantifying many biophysical properties and understanding ecological functions. In this study, we propose a novel multi-directional collaborative convolutional neural network (MDC-Net) that takes the original 3D coordinates and useful features from prior knowledge (prior features) as input, and outputs the semantic labels of TLS point clouds. The MDC-Net contains two key units: (1) a multi-directional neighborhood construction (MDNC) unit to obtain more representative neighbors and enable directionally aware feature encoding in the subsequent local feature extraction, to mitigate occlusion effects; (2) a collaborative feature encoding (CFE) unit is introduced to incorporate useful features from prior knowledge into the network through a collaborative cross coding to enhance the discrimination for thin structures (e.g. small branches and leaf). The MDC-Net is evaluated on five plots from forests in Guangxi, China, with different branch architectures and leaf distributions. Experimental results showed that the MDC-Net achieved an OA of 0.973 and a mIoU of 0.821 and outperformed other related methods. We believe the MDC-Net would facilitate the usage of TLS in ecology studies for quantifying tree size and morphology and thus promote the development of relevant ecological applications.

Synthetic aperture radar tomography (TomoSAR) has gained significant attention for three-dimensional (3D) imaging in urban environments. A notable limitation of traditional TomoSAR approaches is their primary focus on persistent scatterers (PSs), disregarding targets with temporal decorrelated characteristics. Temporal variations in coherence, especially in urban areas due to the dense population of buildings and artificial structures, can lead to a reduction in detectable PSs and suboptimal 3D reconstruction performance. The concept of partially coherent scatterers (PCSs) has been proven effective by capturing the partial temporal coherence of targets across the entire time baseline. In this study, an novel approach based on an iterative sub-network generation method is introduced to leverage PCSs for enhanced 3D reconstruction in dynamic environments. We propose a coherence constraint iterative variance analysis approach to determine the optimal temporal baseline range that accurately reflects the interferometric coherence of PCSs. Utilizing the selected PCSs, a 3D imaging technique that incorporates the iterative generation of sub-networks into the SAR tomography process is developed. By employing the PS reference network as a foundation, we accurately invert PCSs through the iterative generation of local star-shaped networks, ensuring a comprehensive coverage of PCSs in study areas. The effectiveness of this method for the height estimation of PCSs is validated using the TerraSAR-X dataset. Compared with traditional PS-based TomoSAR, the proposed approach demonstrates that PCS-based elevation results complement those from PSs, significantly improving 3D reconstruction in evolving urban settings.

Molecular dynamics (MD) simulations were first employed to achieve the optimal sintering temperature of carboxyl-functionalized graphene (GNS-COOH)-modified polyether ether ketone (PEEK)/polytetrafluoroethylene (PTFE) composites. A model of GNS-COOH/PEEK/PTFE composites was constructed to simulate the effects of different sintering temperatures on the mechanical and tribological properties, as well as their underlying atomic mechanisms. Samples of PTFE composites were prepared and characterized through experimental methods. Results revealed that the sintering temperature significantly affects the intermolecular forces, mechanical properties, and tribological characteristics of the composites. The agglomeration of the PEEK/PTFE composite matrix was effectively mitigated by introducing GNS-COOH. When the sintering temperature was controlled at 360 °C, the compressive strength of GNS-COOH/PEEK/PTFE composites was improved compared to GNS/PEEK/PTFE composites, albeit with a slight reduction in wear resistance. This study provides a theoretical reference for the preparation process and performance evaluation of new materials.

To increase the carbon conversion of char in gasification, this paper aimed to reveal the gasification behaviours of char residues. Char residues with different ash contents in this work were prepared from Shenmu char and Tejing char. Those char residues were gasified by different CO2 gas mixtures at different temperatures. The gasification process of char residue was different from the end stage of the gasification process of the corresponding raw char: the gasification rate of the char residue increased at first and then decreased, whereas the gasification rate of the corresponding raw char kept decreasing during the end stage of gasification. The highest gasification rate was achieved at a lower conversion in the gasification of char residue than in the gasification of the corresponding raw char. Catalytic minerals, high temperature, and high CO2 partial pressure benefited the gasification of gasified char residues. The char residues that contained more catalytic minerals were more reactive in gasification and were less sensitive to changes in temperature and CO2 partial pressure. The Modified Random Pore Model (MRPM) and Random Pore Model (RPM) were used to predict the gasification kinetics of the chars, and the MRPM describes the gasification processes of gasified char residues well.

The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method’s performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions.

Digital array radar (DAR) can fully realize digitalization at both the transmitting and receiving ends. However, the development of freedom at the transmitting end is far from mature. So, the new concept of multi-dimensional waveform coding array has appeared, which can optimize the transmitting resources in space–time/frequency waveform or another dimension. Space–time coding array (STCA) is a typical kind of multi-dimensional waveform coding array, which can make full use of the high degree of freedom at the transmitting end. It realizes emission diversity by introducing a small time delay between different transmission array elements. In this paper, an optimization method for STCA, which combines the array spatial coding at the transmitting end and mismatched filter design at the receiving end, is proposed. This method aims to solve the sidelobe problems of STCA: the inherent resonance phenomenon and the resolution loss problem. The experimental verification and quantitative comparative analysis prove the effectiveness of the proposed method. The resolution is restored to the ideal level under the premise of maintaining the beam-scanning ability and ultra-low sidelobe, and the resonance phenomenon caused by spectrum discontinuity is eliminated simultaneously.

IntroductionTaraxacum mongolicum (TM) is a kind of medicinal and edible homologous plant which is included in the catalogue of feed raw materials in China. It is rich in polyphenols, flavonoids, polysaccharides and other active substances, and shows many benefits to livestock, poultry and aquatic products. The study aimed to assess the potential of TM aqueous extract (TMAE) as a substitute for poultry AGPs. MethodsA total of 240 one-day-old Arbor Acker broilers were randomly assigned to four groups and fed a basal diet (Con) supplemented with 500, 1000, and 2000 mg/kg TMAE (Low, Medium, and High groups). The growth performance of the broilers was measured on day 21 and day 42. At the end of the trial, the researchers measured slaughter performance and collected serum, liver, spleen, ileum, and intestinal contents to investigate the effects of TMAE on serum biochemistry, antioxidant capacity, immune function, organ coefficient, intestinal morphology, flora composition, and short-chain fatty acids (SCFAs). ResultsThe results showed that broilers treated with TMAE had a significantly higher average daily gain from 22 to 42 days old compared to the Con group. Various doses of TMAE resulted in different levels of improvement in serum chemistry. High doses increased serum alkaline phosphatase and decreased creatinine. TMAE also increased the antioxidant capacity of serum, liver, and ileum in broilers. Additionally, middle and high doses of TMAE enhanced the innate immune function of the liver (IL-10) and ileum (Occludin) in broilers. Compared to the control group, the TMAE treatment group exhibited an increase in the ratio of villi length to villi crypt in the duodenum. TMAE increased the abundance of beneficial bacteria, such as Alistipes and Lactobacillus, while reducing the accumulation of harmful bacteria, such as Colidextracter and Sellimonas. The cecum's SCFAs content increased with a medium dose of TMAE. Supplementing broiler diets with TMAE at varying doses enhanced growth performance and overall health. The most significant benefits were observed at a dose of 1000 mg/kg, including improved serum biochemical parameters, intestinal morphology, antioxidant capacity of the liver and ileum, immune function of the liver and ileum, and increased SCFAs content. Lactobacillus aviarius, norank_f_norank_o__Clostridia_UCG-014, and Flavonifractor are potentially dominant members of the intestinal microflora. ConclusionIn conclusion, TMAE is a promising poultry feed additive and 1000 mg/kg is an effective reference dose.

Oil-rich coal is a special coal resource with a tar yield ≥ 7% and a combination of coal, oil, and gas attributes. It is an important resource and development direction for the clean utilization of coal, and has important scientific value in alleviating the shortage of national oil and gas resources and promoting the breakthrough development of coal chemical industry. There are a large number of tar-rich coal resources in the eastern basin of Xinjiang Uygur Autonomous Region. In order to find out the occurrence and distribution of tar-rich coal, the coal accumulation and occurrence characteristics of tar-rich coal were studied by means of sedimentary environment analysis, proximate analysis of coal, ultimate analysis of coal, and Gray-King assay for coal. The geological block method was used to estimate the resources of tar-rich coal. The results show that the tar-rich coal seams in the Santanghu Basin are mainly distributed in the upper section of the Badaowan Formation (J1b2), the Sangonghe Formation (J1s) and the lower section of the Xishanyao Formation (J2x1), the tar yield is about 13.67%, which is high tar-rich coal. The tar-rich coal seams in the Turpan-Hami Basin are developed in the middle section of the Xishanyao Formation (J2x2), and the tar yield is about 7.6%, which are tar-bearing coal and tar-rich coal. The tar-rich coal in the area is generally characterized by ultra-low to medium-low water content, low to medium-low ash content, medium-high to high volatile content, high tar yield, rich in oil generating components such as vitrinite and lipitnite, low degree of coalification, and formed in the lacustrine-deltaic sedimentary environment. The potential of rich oil coal resources in the study area is large. The resources of tar-rich coal in the Santanghu Basin is estimated to be 67.083 billion tonnes above a depth of 2 000 m, and the resources of tar-rich coal in the Turpan-Hami Basin is estimated to be 41.755 billion tonnes above a depth of 2 000 m, and the average thickness of the coal seams is more than 9 m. In order to effectively utilize the development of oil rich coal resources, the technical assumption of Underground Coal Pyrolysis (UCP) is proposed. The UCP refers to the establishment of heat introduction and product production channels in underground coal seams through petroleum engineering technology. Through artificial heating, coal undergoes in-situ carbonization reactions. The pyrolysis products include tar, gas (CH4, H2, light hydrocarbon C2+, etc.), and water. The carbon element in coal is mainly left underground in the form of semicoke, and the generated carbon dioxide can be used to drive oil in the Santanghu and Turpan-Hami Basins. The remaining carbon dioxide can be buried in-situ using the semicoke layer. The UCP achieves a clean conversion of coal resources through “hydrogen extraction and carbon retention”, which is expected to become a technical direction for the low-carbon green development of tar-rich coal.

Myocardial ischemia-reperfusion (MI/R) injury is common in patients who undergo revascularization therapy for myocardial infarction, often leading to cardiac dysfunction. Carbon monoxide (CO) has emerged as a therapeutic molecule due to its beneficial properties such as anti-inflammatory, anti-apoptotic, and mitochondrial biogenesis-promoting properties. However, its clinical application is limited due to uncontrolled release, potential toxicity, and poor targeting efficiency. To address these limitations, a peroxynitrite (ONOO−)-triggered CO donor (PCOD585) is utilized to generate a poly (lactic-co-glycolic acid) (PLGA)-based, biomimetic CO nanogenerator (M/PCOD@PLGA) that is coated with the macrophage membrane, which could target to the ischemic area and neutralize proinflammatory cytokines. In the ischemic area, local produced ONOO− triggers the continuous release of CO from M/PCOD@PLGA, which efficiently ameliorates MI/R injury by clearing harmful ONOO−, attenuating the inflammatory response, inhibiting cardiomyocyte apoptosis, and promoting mitochondrial biogenesis. This study provides a novel insight into the safe therapeutic use of CO for MI/R injury by utilizing a novel CO donor combined with biomimetic technology. The M/PCOD@PLGA nanogenerator offers targeted delivery of CO to the ischemic area, minimizing potential toxicity and enhancing therapeutic efficacy.

Laser impact dynamic compaction experiments and 2D multi-particle finite element method (MPFEM) simulations were carried out on aluminum–copper (Al–Cu) binary-based composite powders. Al–Cu compacts with densities of about 95% were successfully prepared at the maximum laser energy of 1.8 J. Research trends indicate that higher power lasers could nearly achieve the theoretical densities. Analysis of the influence laws of laser energy and copper powder mass fraction on the densities of Al–Cu compacts was carried out, and the effects of microstructure and microhardness of Al–Cu compacts on their mechanical properties were investigated, as well as the stresses and velocities in the dynamic compaction process of Al–Cu composite powders were analyzed. The results showed that the relative densities of composite powder compacts of Al-5 wt%Cu gradually exceeded those of Al-10 wt%Cu and Al-15 wt%Cu with the increase of laser energy. In laser impact dynamic compaction the mechanism of inter-particle joining was solid-state press-welding, and the increase in the hardness of the Al–Cu compacts could be attributed to the inherent hardness of the hard particles. The upper particles have a localized maximum velocity of more than 300 m/s, which met the explosion welding velocity, thus localized cold welding occurred and contributed to the inter-particle connection.

Fuel cells, as clean and efficient energy conversion devices, hold great potential for applications in the fields of hydrogen-based transportation and stand-alone power systems. Due to their sensitivity to load parameters, environmental parameters, and gas supply, the performance monitoring and fault diagnosis of fuel cell systems have become crucial research areas. Electrochemical impedance spectroscopy (EIS) is a widely applied analytical method in fuel cell systems. that can provide rich information about dynamic system responses, internal impedance, and transmission characteristics. Currently, EIS detection is primarily implemented by using simple topologies such as boost circuits. However, the injection of excitation signals often results in significant power fluctuations, leading to issues such as uneven temperature distributions within the cell, unstable gas supply, and damage to the proton exchange membrane. To address this issue, this paper proposes a real-time EIS detection technique for a proton exchange membrane fuel cell (PEMFC) system that connects a lithium-ion battery and injects the load voltage perturbation through a triple active bridge (TAB) converter. By applying the small-signal model of the TAB converter and designing a system controller using a decoupling control method, the PEMFC power remains stable after the disturbance injection across the entire frequency range under tests. Furthermore, the lithium-ion battery can instantly track load changes during fluctuations. The proposed EIS detection method can acquire EIS data in real time to monitor the state of the PEMFC. Simulation results validate the effectiveness and accuracy of the proposed method for EIS detection.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Summary: The efficacy of COVID-19 vaccination relies on the induction of neutralizing antibodies, which can vary among vaccine recipients. In this study, we investigated the potential factors affecting the neutralizing antibody response by combining plasma and urine proteomics and gut microbiota analysis. We found that activation of the LXR/FXR pathway in plasma was associated with the production of ACE2-RBD-inhibiting antibodies, while urine proteins related to complement system, acute phase response signaling, LXR/FXR, and STAT3 pathways were correlated with neutralizing antibody production. Moreover, we observed a correlation between the gut microbiota and plasma and urine proteins, as well as the vaccination response. Based on the above data, we built a predictive model for vaccination response (AUC = 0.85). Our study provides insights into characteristic plasma and urine proteins and gut microbiota associated with the ACE2-RBD-inhibiting antibodies, which could benefit our understanding of the host response to COVID-19 vaccination.

Dislocation of the metatarsophalangeal joint (MTPJ) by trauma commonly occurs in adults. Most dislocations of the MTPJ could be reduced by closed reduction. However, isolated traumatic irreducible dislocation of the fifth MTPJ is an extremely rare injury, particularly in children. We report the case of a 10-year-old boy with irreducible dislocation of the fifth MTPJ who presented with a dorsiflexion injury of the right foot 1 year previously. Closed reduction was attempted but failed. Computed tomography showed the dorsolateral dislocation of the fifth MTPJ. We performed an open reduction and metatarsal bone osteotomy, with a short osteotomy at approximately 0.8 cm. The osteotomy was adjusted to a reduction of the MTPJ and fixation by a lock compression plate. The distal growth plate in the metatarsal bone was protected to avoid pre-closure of the growth plate. There were no instances of dislocation or signs of avascular necrosis of the head of the metatarsal bone. The results of this study demonstrated that open reduction and metatarsal bone osteotomy could be an optional treatment for irreducible dislocation of the fifth MTPJ in children. We should pay more attention to the distal growth plate in the metatarsal bone to avoid pre-closure of the growth plate.

Nickel powders of nominal sizes (i.e., 50 nm, 300 nm, and 2 μm) were pre-coated on copper substrates and then subjected to laser shock processing (LSP) for producing nickel coating. The shockwave consolidation behavior, microstructure, and bonding performance were investigated as a function of particle size. Results indicated that nano-coating had better surface quality, decreased microstructural defects (pores and cracks), and higher bonding strength (approximately twice the hardness value) than its micro-counterpart. Microstructure analysis revealed that nano/submicron particles were consolidated by a mixed solid-liquid sintering behavior, whereas micron particles were consolidated only by solid-state sintering under laser shock. A mixed microstructure of nanocoating was obtained, consisting of solid-state atomic bonds between solid-state sintered particles and amorphous phases between liquid-phase sintered particles. After LSP, the cold shock characteristics of LSP could not only reduce the overall grain growth and oxidation of nano coating, but also induce fine nanocrystalline at the inter-particle interface of the micron coating by dynamic recrystallization.

ABSTRACTWith the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become the third most important data source, after vector maps and imagery. Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science, spatial cognition, and smart cities. However, how to acquire high-quality three-dimensional (3D) geospatial information from point clouds has become a scientific frontier, for which there is an urgent demand in the fields of surveying and mapping, as well as geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the-art of point cloud intelligence, with regard to acquisition equipment, intelligent processing, scientific research, and engineering applications. For this purpose, we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection, as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds. These projects were conducted at the Institute for Photogrammetry, the University of Stuttgart, which was initially headed by the late Prof. Ackermann. Finally, the development prospects of point cloud intelligence are summarized.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations were designed, and the classification accuracy of various approaches was evaluated using the classic convolutional neural networks (CNNs) AlexNet and VGG16. The findings reveal that the commonly employed six-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete nine-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other seven schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant back-scattering information about objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features input into the deep learning network are the power values of polarimetric channels and contain complete polarimetric information. The other two input data schemes are designed to compare the proposed method. The neural network can utilize the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of the power-based scheme. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has a clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

The full Poincaré (FP) beam, encompassing all possible polarization states in its beam cross-section, has demonstrated advantages in various applications. However, conventional FP beams are typically considered as spatially fully coherent, rendering them sensitive to disturbances in the propagation path and susceptible to speckle effects. In this work, we propose an alternative approach to synthesize the optical beam with a FP polarization state through the spatial coherence engineering of a partially coherent beam. In this process, the FP polarization state is initially encoded into the spatial coherence structure of the beam source. We demonstrate that during the encoding process, the vector nature of the beam transitions from the FP polarization state to the spatial coherence structure of the source. However, during the propagation of the partially coherent beam, the vectorness reverts to the polarization state, resulting in the re-emergence of the encoded FP polarization in the output plane. We illustrate that the synthesized FP polarization state, achieved through spatial coherence engineering, is highly robust against obstructions in the propagation path. Furthermore, we examine the effect of the spatial coherence area of the beam on the quality of the recovered FP polarization state. The findings of this work can have valuable applications in optical trapping and optical imaging in complex environments.

BackgroundDespite the well-grounded benefits of physical activity (PA), poor compliance with the PA guidelines has been reported among head and neck cancer (HNC) patients. Mobile health (mHealth)-based interventions can help cancer survivors increase their PA levels and increase the reach or efficiency of rehabilitation services. However, there is limited knowledge about the needs and perceptions of HNC patients regarding these interventions. This study explored the perceptions and needs of HNC patients regarding mHealth-based PA programs before developing such interventions to ensure their improved effectiveness.Study designA constructivist qualitative study.MethodsWe purposively selected 17 adult HNC patients aged 40–80 years to determine their needs and perceptions of future mHealth-based PA programs. Semi-structured face-to-face interviews were conducted, and the data were analyzed via thematic analysis. The report followed the Consolidated Criteria for Qualitative Research Reports guidelines.ResultsFour themes were analyzed from the interview transcripts regarding the needs and perceptions of mHealth-based PA programs: (1) functionality needs; (2) system usage requirements; (3) social support; and (4) perceived barriers and facilitators. HNC patients expect highly customized and specialized mHealth interventions that consider individual factors, address their concerns about security, network, and cost, and prefer improved comfort. Moreover, they expect to receive support from their healthcare providers, families, and peers.ConclusionThe study provides pragmatic ready-to-use recommendations to design interventions for inactive HNC patients to achieve the recommended PA levels. Future mHealth interventions should be tailored according to the needs of the HNC patients by utilizing perceived facilitators and removing perceived barriers to help them engage in PA actively.

Alongside widely accepted geometric information, airborne laser bathymetry (ALB) typically captures the temporal profile sample (waveforms) of both the emitted laser pulses and their echoes. These waveforms provide radiometric properties (backscattering intensity) of sensed targets and assist with precise strip registration, classification of fine ground cover (sediment), and advanced geometric modelling. However, effective intensity data extraction is essential because the intensity provided by the LAS file is mixed with invalid signals, multiple echo intensity, and intermittent intensity variations caused by improper interference of automatic gain control, making it unusable for direct analysis. To address this issue, a flexible index sharing mechanism between waveform, coordinate, and intensity data is constructed for interference intensity tracking. A novel waveform classification-based approach is proposed to efficiently extract ALB intensity by dividing waveform data into six categories using morphological differences and topographic data of waveforms. To ensure accurate analysis, duplicate and invalid waveforms are eliminated, leaving only genuine intensity readings. Additionally, a triple spline fit is employed to restore oversaturated waveform segments that were previously suppressed due to exceeding the device's maximum measurable limit. To address the problem of mixing multiple return intensities, waveforms are decomposed using various decomposition models. This approach ensures that different waveform categories retain their respective varying return intensities. The approach is then tested on an ALB dataset collected using Optech Aquarius around Yuanzhi Island in the South China Sea. The results demonstrate a considerable advancement in data quality when compared to LAS file intensity products with a reduction in maximum deviation of 883 [digital number, DN], standard deviation of 350 [DN], and mean absolute error of 310 [DN].

Wavenumber domain algorithms have unique advantages in processing highly squinted synthetic aperture radar data. This article studies the performance of three commonly used wavenumber domain algorithms including the classical wavenumber domain (CWD) algorithm, extended wavenumber domain (EWD) algorithm, and squint wavenumber domain (SWD) algorithm. First, the wavenumber domain signal expression under the zero-Doppler and acquisition-Doppler reference geometries are both derived. Second, the internal relationship between three wavenumber domain algorithms is analyzed. A new interpretation of the relationship between the three algorithms and an interpolation strategy are given. The analysis not only provides a deeper understanding of the three algorithms, but also provides a basis for comparing them. Then, the performance of the three wavenumber domain algorithms is evaluated from the perspectives of computational complexity, image quality, and geometric position through theoretical analysis and simulation experiments. Aiming at the problem that the range and azimuth profiles are not orthogonal, a method to calculate resolution and extract profile is proposed. The results show that all three algorithms can obtain a well-focused images if full-resolution interpolation is performed, and the computational complexities of CWD and SWD are less than that of EWD.

Abstract Background Cystic angiomatosis is a rare benign disease manifesting as multiple lytic and sclerotic bone lesions, described as the proliferation of vascular and lymphatic channels lined by a single layer of endothelial cells. However, the potential pathogenetic mechanism of the disease still remains unknown. Here, we reported a case of cystic angiomatosis with multifocal bone lesion evaluated by whole exome sequencing. Case description In this presentation, we reported a case of an 11-year-old boy with pain in his chest. Computed tomography (CT) revealed the multiple lytic of the bone in the ribs, clavicle, vertebra thoracalis, skull, mandibula, shoulder blade, etc. The blood test showed ALP to be 393U/L and VEGF to be 287.26 pg/ml. The patient was performed with an open biopsy in the ribs and was diagnosed with cystic angiomatosis. Besides, the whole exome sequencing reported the single-nucleotide substitutions in the coding region of BRIP1, CHEK2, GRM4, and MUC16. Then, the upregulated genes involved CASC15, CENPF, ABCA13, ALK, BLM, and FGFR3. Conclusions In this article, we report a rare case of cystic angiomatosis in a child with abnormal VEGF and ALP reported by peripheral blood examination. The whole exome sequencing could provide the reference for the potential molecular mechanism in the diagnosis and treatment of cystic angiomatosis.

Bizzare parosteal osteochondromatous proliferation (BPOP), first introduced by Nora et al., in 1983, s an uncommon benign bone disease that manifests as a parosteal bone mass that frequently develops in the location of short bones. In our study, we reported an 11-year-old child with BPOP in the second finger of the right hand and the bone lesion was gradually increasing after 14 months of follow-up. Radiographic imaging confirmed an osseous and lobulated mass from the proximal phalanx cortex without the connection with medullary canal of finger. Due to the aggressive nature of the imaging, bone biopsies were performed with ''blue bone'' pathological findings typical of BPOP, with complete resection of the bone exostosis and extended resection of the bone cortex to reduce the recurrence of the lesion. Then, the microarray data of GSE19357 in BPOP was downloaded and analyzed as a result of differentially expressed genes (DEGs), resulting in 11 up-regulated genes including HAPLN1, ACAN, FMOD, DKK3, and P4HA3 with the calculating criteria of P 2. STRING databases were performed to reveal the protein–protein interaction network (PPI) among DEGs. There were 21 nodes and 50 edges with an average local clustering coefficient of 0.624 and PPI enrichment p-value of 3.83e−13. The molecular function in Gene Ontology was highly enriched in Lipoprotein lipase activity, Phospholipase a1 activity, Phosphatidylserine 1-acylhydrolase activity, and Carboxylic acid binding. In Cytoscape, there were three sub-networks of genes with Plugin MCODE, including the relationship of PPARG, FABP4, PRKAR2B, TGFB3 FMOD and DKK3, SULF1, THBS3, HAPLN1 and P4HA3, and LIPG, LPL and PCK1. In this article, we reported a rare case of BPOP in a child with a bone lesion. The DEGs GSE19357 in BPOP were then analyzed and evaluated for the potential molecular mechanism in the diagnosis and treatment of BPOP.

Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity. Here, the authors report a method based on the combination of hydrogel-based tissue transformation with mass spectrometry-based proteomics, that enables proteome profiling with a lateral resolution of 160 µm.

Abstract Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

Heavy metals (HMs) are the toxic pollutants in urban environment, and their sources are complex. Dust might be a good carrier of HMs into ecosystem and human. In this study, 48 dust samples were collected in Nanjing, an industrial city and transportation hub in the Yangtze River Delta, China. The concentrations, spatial distribution, sources and risks of heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) in dust were determined and analyzed. The results showed that although the health risks of some HMs had decreased, Cr, Zn, and Cd had high concentrations and high risks on ecosystem/human. And thus, the total risks of the target HMs were higher than the threshold of non-risks. Especially, children may face the highest possible risks due to the frequent hand-oral ingestion when children play. The hot spot regions of the HMs were mainly in the industrial district in the north, urban, and rural region in south, relating with the industrial, traffic, and agricultural sources, respectively. The analysis for the risks of individual sources further confirmed these sources should be further controlled. The results in this study will provide information on the priority of HMs’ monitoring and source management.

Three-dimensional (3D) object detection utilizes numerous onboard sensors to determine the position, size, and motion information of surrounding objects. Recently, some researchers have utilized HD maps in 3D object detection for LiDAR point clouds. However, existing LiDAR–map fusion detection methods simply take the HD map as an additional input and more prior information in the map is underexplored. To address this limitation, we employ the HD map in data augmentation for LiDAR 3D detection to eliminate the bias of distributions between the augmented training and test sets. Furthermore, we propose a map-enhanced 3D object detection network, called MENet, for LiDAR point clouds. Compared with the standard LiDAR-only detector in bird’s eye view (BEV), MENet extends LiDAR-based baselines by using a map encoder to encode the input rasterized HD map into a BEV feature. In addition, it uses a multimodal fuser to aggregate the map BEV feature with the LiDAR BEV feature extracted in the LiDAR stream. MENet achieved a mean average precision (mAP) of 56.90% and nuScenes detection score (NDS) of 63.43%, improving the mAP and NDS of its LiDAR-only backbone by 4.18% and 2.22%, respectively. Although the HD map is necessary at training time, the map-enhanced 3D detector can work robustly and perform better than the original baseline when there was no map input at test time. Experimental results further indicate that the proposed map plugins can improve the performance of both anchor-based and anchor-free models on different datasets. The code will be open-source at https://github.com/WHU-USI3DV/MENet.

The innovative synthesis of 3,8-dibromo-2,9-dinitro-5,6-dihydrodiimidazo [1,2-a:2′,1′-c]pyrazine and 3,9-dibromo-2,10-dinitro-6,7-dihydro-5H-diimidazo [1,2-a:2′,1′-c][1,4]diazepine is described in this study. The tricyclic fused molecular structures are formed by the respective amalgamation of piperazine and homopiperazine with the imidazole ring containing nitro. Compound 1 and 2 possess excellent high-density physical properties (ρ1 = 2.49 g/cm3, ρ2 = 2.35 g/cm3) due to the presence of a fused ring structure and Br atom. In addition to their high density, they have high decomposition temperatures (Td > 290 °C) which means that they have excellent thermal stability and can be used as potential heat-resistant explosives. Low mechanical sensitivities (IS > 40 J, FS > 360 N) are observed. The twinning structure of 2 was resolved by X-ray diffraction. Non-covalent interaction analysis, Hirshfeld surfaces, 2D fingerprint plot, and Electrostatic potential analysis were used to understand the intramolecular interactions in relation to physicochemical properties. The unique structures of this type of compound provide new potential for the evolution of energetic materials.